How to Use the Nmap Online MCP in LangChain

Q: How do I use Nmap Online tools in a LangChain agent?

First, pip install langchain-mcp-adapters. Then, get the tools from the client and pass them to your agent constructor, like createagent. The agent will then be able to call tools like nmapscan as part of its reasoning chain.

Q: Can I chain multiple Nmap Online tools together with LangChain?

Yes, that's the core idea. For example, your agent can run listsubdomains and then iterate through the results, calling pinghost on each one. LangChain makes it simple to pipe the output of one tool into the input of another.

Build network audit chains with LangChain. Connect Nmap Online tools to automate discovery from start to finish.

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Setup Nmap Online with LangChain

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Build Multi-Step Audit Chains

This MCP server gives your LangChain agent tools for network discovery. Start with `list_subdomains` to find assets, then feed those results directly into `nmap_scan` to check for open ports on each one. The agent decides the next step based on what it finds. You're not just calling single tools; you're building a process. If `nmap_scan` finds an open web port, the chain can automatically trigger `get_http_headers` and `get_page_links` to map out the web application's surface. It's all observable in LangSmith.

Dynamic Footprinting with Agents

Use `dns_lookup` and `whois_lookup` to gather initial intel on a domain. Your agent can then use that information—like nameservers or registrant details—to decide which other tools to run. It’s about making smart, context-aware decisions. You can create chains that trace network paths with `traceroute` and then get location data for each hop using `geoip_lookup`. This builds a complete picture of how traffic flows to your assets, all orchestrated by your agent.

Connect Audits to Your Data

The real power comes when you connect these network tools to other LangChain integrations. Pipe the output of an `nmap_scan` into a SQL database, a vector store, or even a Slack notification tool. Your agent becomes a bridge between your network and your internal systems. This isn't just about running scans. It's about taking action on the results. With this MCP server, you build agents that don't just find problems; they report them, log them, and kick off remediation workflows automatically.

Setup guide

Set up Nmap Online MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Nmap Online tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "nmap-online-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Nmap Online transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Nmap Online MCP in LangChain

First, `pip install langchain-mcp-adapters`. Then, get the tools from the client and pass them to your agent constructor, like `create_agent`. The agent will then be able to call tools like `nmap_scan` as part of its reasoning chain.

Yes, that's the core idea. For example, your agent can run `list_subdomains` and then iterate through the results, calling `ping_host` on each one. LangChain makes it simple to pipe the output of one tool into the input of another.

You can wrap your tool calls in standard Python try/except blocks within a custom chain or use LangChain's built-in error handling. This lets your agent gracefully manage failed pings or timeouts from `traceroute` and decide on an alternative action.

Absolutely. Every tool call your agent makes to the Nmap Online server is traced in LangSmith. You'll see the exact inputs, outputs, latency, and token usage for each step in your audit chain.

Your requests, containing domain names or IP addresses for tools like `nmap_scan`, are sent to our ephemeral V8 sandbox. We don't store your scan results or query history. All processing is stateless and isolated to your specific request.

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